One type of medical image analysis involves decomposing an image into meaningful regions, e.g., imaged anatomical structures versus image artifacts. This process is sometimes referred to as (medical) image segmentation. In some cases, the presence of image noise or other image artifacts may hinder accurate segmentation of anatomical structures of interest. A common approach for automating segmentation may include using a learning based system. A learning based system may be trained to predict a class label probability for each image element (e.g., pixel, voxel, or object parameter). For instance, a shape may be fitted, or a structured output model may be applied to assign a label to each image element based on its predicted label probability. A learning based system may also be trained to estimate, for an image, the locations of boundaries of different structures within the image. In some cases, these estimated boundaries may not align with the boundaries between discrete image elements. The final segmentation may reflect a likely segmentation for a given image.
However, many segmentation boundary locations may be plausible. Even human technicians may select or draw different boundary locations due to ambiguous image data. In addition, boundaries estimated by a trained system may be ambiguous due to a lack of appropriate training examples or due to a sub-optimally trained method. In this case, the trained system may not provide accurate object boundaries. A final segmentation often does not indicate whether the segmentation is a nearly definite accurate representation of a portion of anatomy, or whether an alternative segmentation is almost equally likely to render an accurate representation of anatomy. Further, the final segmentation may not indicate region(s) where the final segmentation boundary was more certain or less certain. Accordingly, a desire exists to understand the statistical confidence (or statistical uncertainty) of a provided segmentation. A desire also exists for generating alternative or aggregated solutions segmentations.
The present disclosure is directed to overcoming one or more of the above-mentioned problems or interests.